Prof. Maode MA

Shenzhen University of Advanced Technology

Prof. Maode Ma, a Fellow of IET, received his Ph.D. from the Department of Computer Science at the Hong Kong University of Science and Technology in 1999. Prof. Ma is a Full Professor in the Faculty of Computer Science and Artificial Intelligence at Shenzhen University of Advanced Technology. Before joining SUAT, he had been a faculty member at Nanyang Technological University and Qatar University for over 25 years. He has extensive research interests in network security, AI security, and wireless networking. He has about 550 international academic publications, which include more than 280 journal papers. His publication has received close to 13,000 citations in Google Scholar. Prof. Ma currently serves as the Editor-in-Chief of the Journal of Communication and Network Security, the International Journal of Computer and Communication Engineering, and the Journal of Communications. He also serves as a Senior Editor for IEEE Communications Surveys and Tutorials, and an Associate Editor for the International Journal of Communication Systems. Prof. Ma is a senior member of the IEEE Communication Society. Prof. Ma has been a Distinguished Lecturer for the IEEE Communication Society from 2013 to 2016 and from 2023 to 2024.

Speech Title: Design of Automatic Incremental Lifetime Learning IDSs

Abstract: Traditional Intrusion Detection Systems(IDSs) provide limited defense against emerging threats, as they rely on static rules or machine learning (ML) models that lack the capacity for real-time updates. The Incremental Lifetime Learning IDS (ILL-IDS) is a new type of IDS to address this limitation by enabling adaptive learning of new attack types. However, ILL-IDS depends heavily on large volumes of high-quality labeled data, making the model update process costly and labor-intensive. In this talk, the Automatic Incremental Lifetime Learning IDS (AILL-IDS) is introduced, which is a novel IDS framework that can significantly reduce the need for labelling data by incremental semi-supervised learning. This approach not only enables AILL-IDS to detect unknown types of attacks and adapt its model dynamically with minimal labeled data but also ensures continuous detection during the model update process, enhancing both speed and accuracy in threat detection in vehicular networks or Internet of Thing (IoT) systems. Experimental results demonstrate that AILL-IDS can achieve a high detection rate of 0.97 and an average F1 score of 0.90, labelling only 5.5% of the total traning data, thereby offering an efficient and scalable solution for securing IoT against emerging cyber threats.

Prof. Adul Rauf 

Nanjing University of Information Science & Technology

Abdul Rauf is currently a full professor at the School of Management Engineering of Nanjing University of Information Science and Technology. He is engaged in research in fields such as business economics, energy economics, environmental economics, stock market volatility, tourism economics, and ICT. He has published over 60 SSCI, SCI, EI and international papers in renowned journals. He received the National Natural Science Foundation of China's Foreign Young Scholars Program in 2024. His vision is to address market dynamics and predict future market behavior through in-depth study of the financial field (energy economy, environmental issues and financial markets). For this reason, he hopes to seek a research position related to market dynamics in a well-known university or research institution. In this position, he can fully utilize the knowledge and skills he has mastered to publish academic papers, hoping to help the research institute continue to enhance its reputation. At the same time, he hopes to constantly improve his own research level. His interests include economics, finance, financial development and investment, environmental policy and law, energy economics, decarbonization, and the Belt and Road Initiative. His assignment is to delimit the financial domain (energy economics, environmental issues, and financial markets) in order to respond to market dynamics and predict behavioral flows. He aims to obtain a professional position in the appropriate field of a dynamic and reputable institution, contribute his knowledge and skills to the advancement of the institution, and provide opportunities for growth and research progress.

Prof. Thippa Reddy Gadekallu

Zhejiang Agriculture and Forestry University

Thippa Reddy Gadekallu is currently working as a Professor at Zhejiang A&F University and as a visiting professor at the Division of Research and Development, Lovely Professional University, Phagwara, India. He has obtained his Bachelors in Computer Science and Engineering from Nagarjuna University, India, in the year 2003, Masters in Computer Science and Engineering from Anna University, Chennai, Tamil Nadu, India in the year 2011 and his Ph.D in Vellore Institute of Technology, Vellore, Tamil Nadu, India in the year 2017. He has more than 15 years of experience in teaching and research. He has more than 400 international/national publications in reputed journals and conferences. Currently, his areas of research include Machine Learning, Internet of Things, Deep Neural Networks, Blockchain, Computer Vision. He has acted as a guest editor in several reputed publishers like IEEE, Elsevier, Springer, MDPI. He is recently recognized as one among the top 2% scientists in the world as per the survey conducted by Elsevier in the years 2021, 2022, 2023, 2024, 2025. He is also recognized as a highly cited researcher, young scientist category, by Clarivate (web of Science) for the period 2017-2022 in the year 2023.

Speech Title: Federated Learning for Big Data- Opportunities, Applications, and Future Directions

Abstract：Federated Learning (FL) offers a decentralized approach to machine learning that keeps data local while training global models, making it ideal for privacy-sensitive Big Data applications. This presentation surveys how FL enhances data acquisition, storage, analytics, and privacy across domains like smart cities, healthcare, and transportation. We discuss real-world implementations, popular FL platforms, and key challenges—including communication efficiency, data heterogeneity, and security threats. The talk concludes with future research directions aimed at scaling FL for real-world Big Data ecosystems, positioning FL as a critical enabler for secure, efficient, and collaborative AI.